# Generalized Poisson distribution

**URL:** <https://discourse.pymc.io/t/generalized-poisson-distribution/6535>\
**Category:** Development\
**Created:** [January 6, 2021, 6:07am UTC](https://discourse.pymc.io/t/generalized-poisson-distribution/6535 "2021-01-06T06:07:56Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![ally-lee](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/ally-lee/32/3046_2.png) [@ally-lee](https://discourse.pymc.io/u/ally-lee)\
**Post date:** [January 6, 2021, 6:07am UTC](https://discourse.pymc.io/t/generalized-poisson-distribution/6535/1 "2021-01-06T06:07:56Z")

</div>

Hey PyMC developers,

For a recent project, I decided to use a Generalized Poisson distribution as it offers a more flexible likelihood for modeling count data than the standard Poisson. I defined a custom discrete distribution in PyMC3 to do this, and I’d love to share what I did with the community. If you’re interested in adding an implementation for the Generalized Poisson, I could open a PR or share my code with you all. I’m also thinking of writing a blog post to give a tutorial on how I defined a custom distribution in PyMC3, which could be useful for your docs. Let me know if you’d like to collaborate on any of this!

Thanks!

---

<div class="post-metadata">

**Author:** ![lewiso1](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/lewiso1/32/2426_2.png) [@lewiso1](https://discourse.pymc.io/u/lewiso1)\
**Post date:** [January 12, 2021, 4:11am UTC](https://discourse.pymc.io/t/generalized-poisson-distribution/6535/2 "2021-01-12T04:11:16Z")

</div>

Not a developer but I’d love to read the blog post!